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Elastic Large Language Model Inference: Demand-Driven Autoscaling and Proactive Cache Pre-warming
Deploying Large Language Models (LLMs) in disaggregated, cloud-native environments such as Kubernetes requires dynamic and elastic autoscaling to accommodate variable demand and control computational costs. However, this approach faces two interrelated challenges.openaire +1 more source
Improved Performance Control of Cloud-Native Microservices in the Edge with Proactive Autoscaling
NOMS 2025-2025 IEEE Network Operations and Management SymposiumBalázs Fodor, Balázs Sonkoly
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Predictscale: Proactive Autoscaling Via Workload Forecasting for Edge Microservices Application
2026 IEEE 26th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)Bablu Kumar +3 more
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Graph Neural Network-Based SLO-Aware Proactive Resource Autoscaling Framework for Microservices
IEEE/ACM Transactions on NetworkingJinwoo Park +3 more
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Proactive Hybrid Autoscaling for Container-Based Edge Applications in Kubernetes
Kaile Zhu +6 moreopenaire +1 more source
2024 33rd Wireless and Optical Communications Conference (WOCC)
Zheng Gen Chen +5 more
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Zheng Gen Chen +5 more
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Proactive Autoscaling for Cloud-Native Applications Using Predictive Resource Allocation
Bablu Kumar +2 moreopenaire +1 more source
Trace-driven proactive autoscaling for cluster workloads via multi-horizon forecasting
Cluster ComputingJinchun Liu, Haoxun Li
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